System and Method for Use of Semantic Understanding in Storage, Searching and Providing of Data or Other Content Information

ABSTRACT

A system and method for using semantic understanding in storing and searching data and other information. A linearized tuple-based version of a conceptual graph can be created from a user input. A plurality of conceptual graphs, or portions thereof, can be compared to determine matches. An associative database can be created and/or searched using a hierarchy of conceptual graphs in tuple format, so that the data storage and searching of such database is optimized. The associative database can be used to integrate data from multiple different sources; form part of an Internet or other search engine; or used in other implementations. Also disclosed herein is a system and method for use of semantic understanding in searching and providing of content is described herein. In accordance with an embodiment, the system comprises a Syntactic Parser (SP) or statistical word tokenizer for data retrieval and parsing; a Syntax To Semantics (STS) transformational algebra-based semantic rule set, and an Associative Database (ADB) of linearized tuple conceptual graphs (TCG), utilizing a conceptual graph formalism. Data can be represented within the ADB, enabling both fast data retrieval in the form of semantic objects and a broad ranging taxonomy of content.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.16/132,777, filed on Sep. 17, 2018; which application was a continuationof U.S. patent application Ser. No. 14/510,366, filed on Oct. 9, 2014(now U.S. patent Ser. No. 10/078,633, issued Sep. 18, 2018); whichapplication was a continuation of U.S. patent application Ser. No.12/905,314, filed on Oct. 15, 2010, (now U.S. Pat. No. 8,880,537, issuedNov. 4, 2014) entitled “SYSTEM AND METHOD FOR USE OF SEMANTICUNDERSTANDING IN STORAGE, SEARCHING AND PROVIDING OF DATA OR OTHERCONTENT INFORMATION”. Each of these applications claim the benefit ofpriority to U.S. Provisional Application No. 61/378,819, filed Aug. 31,2010, entitled “SYSTEM AND METHOD FOR USE OF SEMANTIC UNDERSTANDING INSEARCHING AND PROVIDING OF CONTENT”; and U.S. Provisional PatentApplication No. 61/253,039, filed on Oct. 19, 2009, entitled “SYSTEM ANDMETHOD FOR STORAGE AND SEARCHING OF DATA AND OTHER INFORMATION.” Each ofthese applications is incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

Embodiments of the invention are generally related to data storage anddata search techniques, and are particularly related to systems andmethods for using semantic understanding and conceptual graph techniquesin storage, searching, retrieving and providing of data or other contentor information.

BACKGROUND

Several techniques have been investigated over the years with the goalof helping promote human-computer interactions, particularly to allowusers to have more human-like interactions with computers. In thecontext of verbal or written interaction, one approach is to enable thecomputer to understand phrases provided in a natural language format asuttered or typed by humans. An important factor in computerunderstanding then is to ensure the computer can, to a reasonableextent, understand what is being said by the user.

Various attempts at addressing this problem have been considered. Forexample, conceptual graphs have been employed to capture the meaning andcontent of a human utterance. Additional information describing variousaspects and examples of conceptual graphs, link grammars, andassociative databases are described in “PRACTICAL NATURAL LANGUAGEPROCESSING QUESTION ANSWERING USING GRAPHS”, PhD dissertation by GilEmanuel Fuchs, University of California Santa Cruz, December 2004, whichis herein incorporated by reference. However, while conceptual graphscan be powerful constructs for capturing the meaning of language, suchgraphs must typically be created from natural language using some formof artificial intelligence and/or manual input by a skilled operator.This has generally limited the usage of conceptual graphs in commercialapplication environments.

As the amount of data stored and accessed by users increasesconsiderably every day, techniques are desired that allow for efficientstorage and searching of such data, in a manner that allows for ease ofuse by the user, and also provides for additional industrial uses. Theseare some of the areas that embodiments of the present invention areintended to address.

BRIEF SUMMARY OF THE INVENTION

Disclosed herein is a system and method for using semantic understandingin storing and searching data and other information. In accordance withan embodiment, techniques are provided to allow capturing andinterpreting semantics or meaning from a user input. A linearizedtuple-based version of a conceptual graph can be created from the userinput. A plurality of conceptual graphs, or portions thereof, can becompared to determine matches. An associative database can be generatedand/or searched using a hierarchy of conceptual graphs in tuple format,so that the data storage and searching of such database is optimized.The associative database can be used to integrate data from multipledifferent sources; form part of an Internet or other search engine; orused in other implementations. In accordance with an embodiment,integration can be performed in an offline manner, to gather informationor data in a centralized location and to generate new semanticconnections between the information or data; and in an online manner inwhich the system uses semantic rendering to provide real-time responsesto input data; or to assess degree of closeness of relevance between twosets of text. Embodiments of the invention are particularly suited toefficiently storing and searching vast amounts of textual data.

Also disclosed herein is a system and method for use of semanticunderstanding in searching and providing of content. In accordance withan embodiment, the system comprises a Syntactic Parser (SP) orstatistical word tokenizer for data retrieval and parsing; a Syntax ToSemantics (STS) transformational algebra-based semantic rule set, and anAssociative Database (ADB) of linearized tuple conceptual graphs (TCG),utilizing a conceptual graph formalism. Data can be represented withinthe ADB, enabling both fast data retrieval in the form of semanticobjects and a broad ranging taxonomy of content, e.g. advertisingcategories. Each semantic object contains all the related terms andphrases articulating a specific subject, enabling automaticcategorization of any given page. This semantic approach can be used ina variety of ways, for example to improve the ability to serve ads basedon the meaning of a website's page content. By semantically analyzingthe web pages, the system can properly understand and classify themeaning and sentiment of any given digital text, and accordingly ensurethat the web page receives the most appropriate advertising. The systemcan also ensure that campaigns are placed on pages which arecontextually relevant to them, whatever the format and medium. Forexample, the semantic approach can be used to analyze an advertiser's adand the website it links to, in order to identify the most relevantmatches.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows an illustration of system, together with various phases ofdata storage and/or searching in accordance with an embodiment.

FIGS. 2-6 show various illustrations of conceptual graphs.

FIGS. 7A-B show examples of conceptual relation types which can be usedin accordance with various embodiments.

FIG. 8 shows various illustrations of link grammar lexicon entries.

FIGS. 9-10 illustrate typical sentences and the linkages produced by alink grammar lexicon and the link grammar methodology.

FIGS. 11A-B show examples of link grammar connection/linkage elementswhich can be used in accordance with various embodiments.

FIG. 12 illustrates an algebra for the use of semantic rules, inaccordance with an embodiment.

FIG. 13 illustrates examples of semantic rules which can be used inaccordance with an embodiment.

FIG. 14 illustrates symbolically how patterns are stored in anassociative database, in accordance with an embodiment.

FIG. 15 shows a flowchart of a process for transforming an input text toa semantic rendering, in accordance with an embodiment.

FIG. 16 shows a flowchart of a process for creating a linearized tuplebased rendering of a TCG in accordance with an embodiment.

FIG. 17 illustrates a process for providing full or partial comparisonof linearized TCG in accordance with an embodiment.

FIG. 18 shows a flowchart of a process for providing full or partialcomparison of linearized TCG in accordance with an embodiment.

FIG. 19 shows a flowchart of a process for storing and retrieving textwithin a database, the text having a semantic rendering or meaning in aTCG, in accordance with an embodiment.

FIG. 20 shows a flowchart of a process for storing and retrieving textwithin a database according to a semantic hierarchy in accordance withan embodiment.

FIG. 21 shows an example of a natural language query processor inaccordance with an embodiment.

FIG. 22 shows a system for use of semantic understanding in searchingand providing of content, in accordance with an embodiment.

FIG. 23 shows a flowchart of a method for use of semantic understandingin searching and providing of content, in accordance with an embodiment.

DETAILED DESCRIPTION

As described above, techniques have been investigated over the yearswith the goal of helping promote human-computer interactions,particularly to allow users to have more human-like interactions withcomputers. In the context of verbal or written interaction, one approachis to enable the computer to understand phrases provided in a naturallanguage format as uttered or typed by humans. An important factor incomputer understanding then is to ensure the computer can, to areasonable extent, understand what is being said by the user. As theamount of data stored and accessed by users increases considerably everyday, techniques are desired that allow for efficient storage andsearching of such data, in a manner that allows for ease of use by theuser, and also provides for additional industrial uses.

In accordance with an embodiment, a system and method for use ofsemantic understanding in searching and providing of content isdescribed herein. In accordance with an embodiment, the system comprisesa Syntactic Parser (SP) or statistical word tokenizer for data retrievaland parsing; a Syntax To Semantics (STS) transformational algebra-basedsemantic rule set, and an Associative Database (ADB) of linearized tupleconceptual graphs (TCG), utilizing a conceptual graph formalism. Datacan be represented within the ADB, enabling both fast data retrieval inthe form of semantic objects and a broad ranging taxonomy of content,e.g. advertising categories. Each semantic object contains all therelated terms and phrases articulating a specific subject, enablingautomatic categorization of any given page. This semantic approach canbe used in a variety of ways, for example to improve the ability toserve ads based on the meaning of a website's page content. Bysemantically analyzing the web pages, the system can properly understandand classify the meaning and sentiment of any given digital text, andaccordingly ensure that the web page receives the most appropriateadvertising. The system can also ensure that campaigns are placed onpages which are contextually relevant to them, whatever the format andmedium. For example, the semantic approach can be used to analyze anadvertiser's ad and the website it links to, in order to identify themost relevant matches.

Techniques are provided to allow capturing and interpreting semantics ormeaning from a user input. A linearized tuple-based version of aconceptual graph can be generated from the user input. A plurality ofconceptual graphs, or portions thereof, can be compared to determinematches. An associative database can be generated and/or searched usinga hierarchy of conceptual graphs in tuple format, so that the datastorage and searching of such database is optimized. The associativedatabase can be used to integrate data from multiple different sources;form part of an Internet or other search engine; or used in otherimplementations. In accordance with an embodiment, integration can beperformed in an offline manner, to gather information or data in acentralized location and to generate new semantic connections betweenthe information or data; and in an online manner in which the systemuses semantic rendering to provide real-time responses to input data; orto assess degree of closeness of relevance between two sets of text.Embodiments of the invention are particularly suited to efficientlystoring and searching vast amounts of textual data. In accordance withan embodiment, the system includes one or more components or processes,including:

-   -   Transformation of an input text into a semantic rendering, such        as a conceptual graph (CG) rendering, or in accordance with an        embodiment a tuple conceptual graph (TCG).    -   Optimal storage of such conceptual graph or TCG rendering in a        manner that is computationally efficient, for example in        accordance with an embodiment as a linearized set of TCG tuples.    -   Tools for comparing, either partially and/or fully, the semantic        renderings expressed by the conceptual graphs or TCGs, to        determine partial and/or complete matches respectively between        two TCGs, as a precursor to matching two semantic renderings, or        creating new semantic relationships.    -   Storing of the semantic renderings, for example as a        hierarchical plurality of TCGs, within an associative database,        so that the information in the associative database allows for        easy searching of TCGs, minimizing the storage needs, while at        the simultaneously maximizing the degree to which relationships        can be expressed, and the functionality of the data therein.    -   Use of an associative database, including a hierarchy of TCG        therein, to create new semantic renderings, which may not exist        in any particular input text or input source, but which        themselves provide value, such as information providing, and        advertising that is semantically related to the input text.        These new renderings can be obtained either by joining        complementary disparate bits of info (combinational) or by        deductive agglomeration of data, driven by outside rules, i.e.        those stored separate from the actual running code of the        program.

FIG. 1 shows an illustration of system, together with various phases ofdata storage and/or searching in accordance with an embodiment. As shownin FIG. 1, the system is configured to receive a user input 10. Suchinput can be in a natural language or written text format, as expressedby the user in the form of a statement, or a question. In accordancewith an embodiment, the system can provide interfaces for receiving suchinput and/or communicating output to other systems. A parser orstatistical word tokenizer together with a link grammar lexicon 14, isused to derive linkages 18 within the user input. An algebraictransformer together with a semantic rule set 24, is then used totransform, per semantic rules 26, 27 each linkage to one or more TCGcomponents 30, such as a TCG relationship with optional variables. Eachsemantic rule can output one or more TCG relationships or components.All of the TCG components for the input text are then collected andoptionally sorted to form a tuple based conceptual graph (TCG) 34. TheTCG can be compared with other TCG 42, 44 in a semantic network and/orassociative database 40, using full or partial matching techniques 43.TCGs can also be joined to create new TCGs 48 for which information maynot have previously been provided by any input source. Depending on theparticular implementation (i.e. whether the data input is being added tothe database, or searched within the database) the TCG can be storedwithin the database at an appropriate location, or used to findappropriate data matches within the database, or to provide a result tothe user, for example in a natural language or written text format 52.

Introduction to Conceptual Graphs

As described above, in accordance with an embodiment, a tuple basedconceptual graph (TCG) can be generated corresponding to a textualinput. To better describe the use of TCG, a brief introduction to theuse of conceptual graphs (CG) is provided herein.

A CG is useful in pictorially capturing the meaning of a language.Generally speaking, a CG can be considered a connected bipartite graphin which the two kinds of nodes of the bipartite graph are concepts, andconceptual relations. Every conceptual relation has one or more arcs,each of which must be linked to some concept. If a relation has n arcs,it is said to be n-adic, and its arcs are labeled 1, 2, . . . n. Asingle concept by itself may form a CG, but every arc of everyconceptual relation must be linked to some concept or another CG in theTCG rendering methodology.

FIG. 2 shows a CG 102 of a typical sentence. As shown in FIG. 2, conceptnodes are bounded in boxes, while conceptual relations are circumscribedby circles. Concept nodes are linguistic entities such as, [a girl],[the act of eating], [a pie], [the concept of fastness]. Conceptualrelations relate one or more concepts. Examples of conceptual relationsare: (the agent relation), (the manner relation), and (the objectrelation). Although the arcs are typically directed from one conceptnode to another, for ease of illustration they are not shown as such inthis figure, which merely show which concept nodes are connected towhich conceptual relations. From this example, the relations can be readas follows: The agent of the action eating is the girl Sue. The mannerof the action eating is fast. The object of the action eating is a pie.(Note that “object” here is not used as in linguistic grammaticalstandard term, but rather as a semantic relation—just like “agent”). Theconcept node [Eat] has three arcs coming out of it, which shows thatthis concept node participates in three distinct conceptual relations.

FIG. 3 shows another example of a CG 106, which reads: A monkey iseating a walnut with a spoon made out of the walnut's shell. Besides itspictorial representation, a CG can also be described in a Sowa linearform. To accomplish this, some concept node must be picked as the headof the Sowa linear expression. Usually, the concept node with the mostarcs linked to it makes the best choice for the head. This produces thesimplest CG. Picking [EAT] in the above example for the head yields thefollowing Sowa linear form.

[EAT] -  (AGNT) --> [MONKEY]  (OBJ) --> [WALNUT : *x]  (INST) -->[SPOON] --> (MATR) --> [SHELL] ← (PART) ←  [WALNUT : *x]

As used above, the symbol x is used as a variable to denote anunspecified individual of type [WALNUT]. Both instances must be thesame; hence, in this instance x is a binding variable. An alternativetuple-based notation can be used, in which the binding variable is notnecessary. In accordance with this convention, a rose is a rose is arose. All occurrences of a concept node are considered the same, unlessdifferentiated. In accordance with an embodiment, one walnut is firstdifferentiated from another with a number designator, which leads to:

[EAT] -  (AGNT) --> [MONKEY]  (OBJ) --> [WALNUT.1]  (INST) --> [SPOON]--> (MATR) --> [SHELL] <-- (PART) <--  [WALNUT.2]

The above Sowa linear CG can be read as: A monkey is eating a walnut,with a spoon made from a shell of another walnut. Alternatively, insteadof the concept node [EAT] and concept node [SPOON] could be used thehead, which would produce the following notation:

[SPOON] -  (INST) <-- [EAT] -  (OBJ) --> [WALNUT] --> (PART) --> [SHELL:*y].  (AGNT) --> [MONKEY] ,  (MATR) --> [SHELL : *y]

In accordance with embodiments that uses a tuple based notation, the CGshown above can be rendered as a tuple based conceptual graph (TCG) asfollows:

@CG1 : {  AGNT (EAT, MONKEY),  OBJ (EAT, WALNUT),  INST (EAT, SPOON), MATR (SPOON, SHELL),  PART (WALNUT, SHELL) }

The tuple notation behaves as though all the conceptual relations aresimultaneously the head of the CG, without taking favorites, or makingany less accessible for a searching agent. Any subset of the CG can beisolated and used as a means for search and retrieve, or a JOIN withanother utterance. At the same time, no foreign variables have beenintroduced. The ‘x’ and ‘y’ from the first order logic rendering werenot present anywhere in the common everyday usage of the Englishsentence.

It will be noted that in the above rendering there is usage of a ‘1’ and‘2’, which could be confused with variables. However, these are notstrictly variables, but are instead instance designators (i.e., oneshell is different from the other shell). In the logic rendition, thereis a “something” which has a value (hence a true variable).

There are several different canonicity preserving operations for CGs,including the JOIN operation. When two separate CGs have a commonconcept node, they may be JOINed by merging the identical concept nodestogether. Consider the two Conceptual Graphs 110 shown in FIG. 4. Asshown therein, the concept node [GIRL], and the concept node [PERSON:Sue], can be merged after the node [PERSON: Sue] is restricted to thenode [GIRL: Sue]. After removing redundant links, the resulting CG 114shown in FIG. 5 is obtained. The JOIN operation allows for twocomplementary CGs to be JOINed. However, simply because two CGs arecompatible, does not mean that they describe the same event. Similarly,just because a JOIN can occur, does not mean that it should. In theexample above, a girl other than Sue might be eating a tomato fast.

A CG can be considered a collection of relations over concept nodes. Inaccordance with embodiments, recasting a standard CG in a tuple basednotation as a TCG makes it more compact, and also facilitates matching.For example, the CG 118 shown in FIG. 6 has 10 nodes, 10 edges, and 1cycle. When recast as a TCG, it has 5 nodes, no edges, and no cycles:

@CG1 : {  AGNT (EAT, MONKEY),  OBJ (EAT, WALNUT),  INST (EAT, SPOON), MATR (SPOON, SHELL),  PART (WALNUT, SHELL) }

The TCG form allows the nodes to be more specific, and as such easier tomatch. Subgraph matching also becomes easier, and can be performed inlinear time, rather than exponential. As far as possible, graphs areparsed as sets. As such, the order of their “arms” (or therelationships) can appear in any order, without loss of content. Therelations can be sorted based on their lexicographic value, breakingties with argument order. Upon comparison, it is not necessary tobacktrack, and processing of the relations (i.e. tuples) is performed inorder of processing, which is proportional to the number of clauses andis by definition, the linear cost.

Any tree, or graph, or any connected component structure is expressibleas a TCG, since even in the absence of relations over the nodes, themere connection is the most primitive (and only) relation; that is, the(CONNECTED) relation. A CG can be considered a collection of JOINS on aset of relations. The relations are an ordered n-tuple of concept nodeswith a relation label. The nodes (the concepts, which are arguments ofthe conceptual relations) are stored in a partially ordered hierarchy.Each node needs be stored only once, and each use of it is a pointer,not another copy. In addition to those conceptual relations illustratedabove, examples of other conceptual relation types 120 are shown in FIG.7, and are further described in “PRACTICAL NATURAL LANGUAGE PROCESSINGQUESTION ANSWERING USING GRAPHS”, PhD dissertation by Gil Emanuel Fuchs,University of California Santa Cruz, December 2004, which is hereinincorporated by reference; although it will be evident that additionaland/or different conceptual relation types can be used in accordancewith other embodiments.

Introduction to Link Grammar

As described above, in accordance with an embodiment, the system uses alink grammar lexicon to generate a syntax intermediary from an inputtext, which is subsequently converted to a semantic rendering. To betterdescribe the use of the link grammar lexicon, a brief introduction tothe use of link grammar is provided herein.

FIG. 8 shows pictorially an example 130 of a linking requirement for afew simple words. As shown in FIG. 8, each of the labeled shapes is aconnector. A connector is satisfied when connected to a matchingconnector with the same label but pointing in the other direction. InFIG. 8 there are labeled connectors, which for purposes of illustrationhave different shapes. For example, the connector of the S variety hasone type of a shape when it points to the right, and another when itpoints to the left. These shapes are complementary, i.e. are made for,and can only connect with, each other (e.g. a right pointing O would notbe able to hook to a left point S. Similarly, only a right pointing Scan hook to a left pointing S). A linkage between words can only be madeif they agree on what they are looking for. An example of the abovewould be: a noun would link to a determiner on the left that is lookingfor a noun on the right. The links are not directed links, i.e. there isno meaning to where the link is coming from or going to. The link isjust a connection between two items, i.e. if a word has a right pointinghalflink, then it can only hook to words with left pointing half-linkswhich are to its right. A word with a right pointing half-link cannothook up to words on its left. When more than one connector emanates fromthe black dot in the box, exactly one connector must be used. Connectinga pair together is equivalent to drawing a link between a pair of words.

FIG. 9 shows a satisfied linked sentence 134. An ungrammatical sentence,theoretically, should not be able to be satisfied. Of course, inpractice, this is not really the case, but the attempt is to achieve agrammar that can capture most situations. In accordance with anembodiment, a dictionary of linking requirements is provided in thechosen language, for example:

words formula a, the D+ snake, cat D− & (O− or S+) Mary O− or S+ ran S−chased S− & O+

The formulas in the linking requirement dictionary comprise theoperators &, or, parentheses, and the connector names. The + and −designate the direction of the connector in relation to the words towhich it is attached. The & operator requires both conjucts to besatisfied, whereas the or operator requires exactly one of the disjunctsto be satisfied. The order of the arguments of the & is significant. Thefarther left a connector is in the expression the closer a binder it is.For example, in FIG. 9 the word snake is closer to its determiner (leftpointing D− link), than the word for which it is an object (leftpointing O−). There are many ways to combine the links of the satisfiedlinkage, and therefore cause the linkage to be unsatisfied. For example,the left determiner connection of ‘snake’ could go all the way to ‘the’instead of binding with the ‘a’. That would result in an unsatisfiedlinkage. In this particular case it would also violate the requirementthat the left-word of the ‘O’ link must be farther out than theleft-word of the ‘D’ link.

FIG. 10 shows an example of a sentence as it might be parsed using alink grammar lexicon and methodology. In the example 140 shown in FIG.10, the sentence “A girl eats a pie fast” has a number of linkages asdetermined by the link grammar lexicon. In accordance with anembodiment, semantic rules can then be applied to this syntax, to allowthe sentence to be expressed as a TCG shown below:

@CG1 : {  AGNT (GIRL, EAT),  OBJ (EAT, PIE),  MANR (EAT, FAST) }

In addition to those linkage elements illustrated above, examples ofother linkage elements 142 are shown in FIG. 11, and are furtherdescribed in “PRACTICAL NATURAL LANGUAGE PROCESSING QUESTION ANSWERINGUSING GRAPHS”, PhD dissertation by Gil Emanuel Fuchs, University ofCalifornia Santa Cruz, December 2004, which is herein incorporated byreference; although it will be evident that additional and/or differentlinkage grammar lexicons and/or linkage elements can be used inaccordance with other embodiments.

FIG. 12 illustrates an algebra for the use of semantic rules, inaccordance with an embodiment; while FIG. 13 illustrates examples ofsemantic rules which can be used in accordance with an embodiment. Itwill be evident that, in addition to the examples shown herein, thesystem can use other linguistic elements in its link grammar lexicon,and other semantic rules, to best address the needs of particularimplementations.

As shown in FIG. 12, an algebra can be defined and used to configure thesystem to apply the semantic rules by e.g. defining .L as the left wordof a link; .R as the right word of a link; and Δ_(L) and Δ_(R) asinstructions to the system to follow one of the links on the left orright respectively.

As shown in FIG. 13, a plurality of semantic rules 146 can then bedefined and used to configure the system to apply the semantic rules bye.g. as shown by rule 148 for creating a (subject verb object)transformation.

The above algebra and rules are provided for purposes of illustration.Additional and/or different algebra and/or semantic rules can be used inaccordance with other embodiments.

Introduction to Associative Databases

In accordance with an embodiment, the system uses an associativedatabase to store a plurality of conceptual graphs, or TCGs. Within theassociative database, entries are stored as nodes according to ahierarchy, such as one or more of a concept hierarchy, type hierarchyand relationship hierarchy. The hierarchies can be stored togetherwithin the database, or stored separately in the form of lookup tablesor dictionaries or separate database structures. Before the systemreceives an input text, the type hierarchy, and relationship hierarchyare pre-defined. Hierarchies can be modified as necessary to suitparticular implementations. As input text are received into the systemand loaded into the database, the object hierarchy is populated with TCGcorresponding to those input text.

FIG. 14 illustrates symbolically how patterns (TCG or otherwise) arestored in an associative database, in accordance with an embodiment,from more general, to more specific. When the system attempts to matchthe TCG for an input text, with the TCG already stored in the database,it starts at the top of the hierarchy, and moves downward through thehierarchy to determine an appropriate match. In accordance with anembodiment, the system performs matching by looking for full or partialmatches between relationships within the TCG, and creates new TCGs andcorresponding semantic understandings using CG-JOIN operations. Thesetechniques are described in further detail below.

Additional information describing various aspects and examples ofconceptual graphs, link grammars, and associative databases aredescribed in “PRACTICAL NATURAL LANGUAGE PROCESSING QUESTION ANSWERINGUSING GRAPHS”, PhD dissertation by Gil Emanuel Fuchs, University ofCalifornia Santa Cruz, December 2004, which is herein incorporated byreference. It will be evident that other types of, e.g. link grammars,lexicons and rules, can be used in accordance with variousimplementations, and that the invention is not limited to the preciseforms disclosed herein.

Transformation of Input Text to Semantic Rendering

In accordance with an embodiment, the system can include a component orprocess for expressing an input text to have a semantic rendering ormeaning, comprising including receiving an input text expressed as aplurality of sentences, each of which sentences includes a plurality ofwords, parsing the input text using the link grammar methodology andlexicon, to determine a syntax within the input text, including linkagesbetween the words, and using a set of semantic rules to transform thesyntax to a semantic rendering or meaning, wherein each rule maps aparticular linkage type or words in the input sentence to a semanticrelationship component, or to a concept node participating in a semanticrelationship component, respectively.

FIG. 15 shows a flowchart of a process for transforming an input text toa semantic rendering, in accordance with an embodiment. As shown in FIG.15, in steps 202, an input text is received into the system. This can bein the form of a user request, a query to a search engine, a retrievalof text information as part of an offline process, or any other form oftext input. In step 204, a link grammar lexicon and methodology is userto parse or break down the input text into a series of words that arelinked together by linkages. Any satisfactory link grammar lexicon canbe used, including different lexicons for different languages.Generally, the link grammar lexicon specifies a hundred or so differentrelationships between words, which is sufficient for most purposes. Thelink grammar lexicon can be augmented with additional relationships asnecessary. In step 206, a plurality of transformative semantic rules arethen automatically selected which apply respectively to the linkagesgenerated in the previous step. Different linkages will generallyrequire different rules to allow that linkage to be expressed in asemantic rendering. The rules are algebraic, in that they can be addedor applied in a successive manner if they are applicable. In step 208,the linkages produced by the link grammar are analyzed using theselected plurality of rules. In step 210, once all of the linkages havebeen transformed using their equivalent rules, a semantic rendering canbe output and/or stored, which is equivalent to the input text, butwhich has a CG-like semantic rendering or meaning. In accordance with anembodiment, the semantic rendering output is stored as a CG in anassociative database, including the use of any of the additionaltechniques described below.

Generation and Linearization of Conceptual Graphs

In accordance with an embodiment, the system can include a component orprocess for creating a linearized tuple based rendering of a conceptualgraph (TCG) for use in expressing an input text as having a semanticrendering or meaning, including receiving a plurality of semanticrelationships expressed as a conceptual graph and corresponding to theinput text, and storing the plurality of tuples as a tuple conceptualgraph (TCG) together with a unique name or other TCG identifier.

FIG. 16 shows a flowchart of a process for creating a linearized tuplebased rendering of a TCG in accordance with an embodiment. As shown inFIG. 16, in step 212, an input text is received into the system, and itswords parsed for linkages and transformed, using a link grammar lexiconand semantic rules, and in accordance with the process described abovein FIG. 15. In step 214, the result of parsing and transforming is thateach link is mapped to one or more tuple relationship, together withoptional variables. A tuple relationship with its optional variables canbe likened to a single line item of the pictorial CG shown at theoutset. In step 216, all of the tuple relationships are assembled in aninterim form of TCG that includes the plurality of tuple relationshipsand which completely mimics the pictorial CG and characterizes the inputtext in a conceptual manner. However, at this point there may beduplicates or redundancies, and similar input text can produce quitedifferent sets of tuple relationships. In step 218, where appropriate,some relationships are folded or merged, which reduces the overall sizeof the TCG, and the remaining relationships are sorted, for examplealphabetically (with argument sorting as tie breaks). In step 220, thelinearized TCG is given a unique TCG name, and output or stored forsubsequent use, for example in an associative database.

To consider the example input text shown in FIG. 10, i.e. “A girl eats apie fast”. When the system parses this input text using the link grammarlexicon, it determines the linkages therein, namely D (two), S, O and MVlinkages. In accordance with an embodiment, the D determiner links areignored. The S link is then transformed, using one or more of thesemantic rules, (such as, e.g. the rules shown and described above inFIG. 13), to yield an AGNT tuple relationship:

-   -   AGNT (Girl, Eat)

The O link is then transformed, again using one or more of the semanticrules, to yield an OBJ tuple relationship:

-   -   OBJ (Eat, Pie)

The MV link is then transformed, again using one or more of the semanticrules, to yield a MANR tuple relationship:

-   -   MANR (Eat, Fast)

Finally, each of the tuple relationships generated above are assembledinto a single, perhaps intermediate form of TCG:

@CG1 : {  AGNT (Girl, Eat)  OBJ (Eat, Pie)  MANR (Eat, Fast) }

Where appropriate, some relationships can be folded or merged, whichreduces the overall size of the final TCG, and the remainingrelationships are sorted, for example alphabetically:

@CG1 : {  SVO (Girl, Eat, Pie)  MANR (Eat, Fast) }

The linearized TCG is then output or stored for subsequent use, forexample in an associative database.

Full and/or Partial Comparison of Linearized Tuple-Based ConceptualGraphs

In accordance with an embodiment, the system can include a component orprocess for comparing a plurality of tuple conceptual graph (TCG),including matching a first tuple conceptual graph (TCG), together with afirst name or other TCG identifier and a first set of linearized tuples,with a second TCG to determine a full or partial match results betweenthe tuples; and reporting the results of the full or partial match. Inaccordance with an embodiment, the CG compare operator between twocandidate TCGs (which answers the question: is TCG1 more general thanTCG2?) can be summarized by the following algorithm;

-   -   1. Bring both TCGs into a canonical representation format, which        includes sorting tuples alphabetically, and by first argument        upon ties, second argument upon further ties, etc.    -   2. Set the NOT-EQUAL flag to false.    -   3. If TCG1 has more tuples than TCG2, it cannot be        more-general-than TCG2. Abort early, return (false).    -   4. If TCG1 has a relationship which is not in TCG2, abort early,        return (false).    -   5. For each relationship in TCG1 do:        -   a) Find a candidate matching relationship in TCG2 (i.e. same            tuple name). If not found, then abort early, return (false).            Else continue.        -   b) For each argument in the relationship do i. If argument            in TCG1 is equal OR is ontologically more general than the            argument of equivalent place in TCG2, then continue, else            exit from this loop level, do not abort, but go on to item            (c). If not exited early and argument of TCG1 was            ontologically more general (as opposed to equal) then set            the NOT-EQUAL flag to true.        -   c) If previous attempted relationship failed, try another            candidate in TCG2 (i.e. still same tuple name, if any are            still left) and go to (b), if none left, abort early, and            return (false).        -   d) If previous attempted relationship succeeded, then move            on to next relationship in TCG1, proceed with (5).    -   6. If the NOT-EQUAL flag is false, then TCG1 and TCG2 are equal,        otherwise return (true).

If the Q1 query TCG is compared to the declarative TCG, it will come outas more-general-than, and as such find it as an answer.

FIG. 17 illustrates a process for providing full or partial comparisonof linearized TCG in accordance with an embodiment. As shown in FIG. 17,in accordance with an embodiment, the TCG can be organized or sorted insuch a manner (generally hierarchically, for example as an associativedatabase) so that one, e.g TCG is logically below another TCG within abranch of the hierarchy if that (lower or child) TCG is a more specificversion of its (higher or parent) TCG. As the hierarchy is descendeddown a particular branch, the related TCG become increasingly morespecific. In accordance with an embodiment, this allows a TCG 221 to besuccessively compared 223, 224 to the other TCG 222 (e.g. in anassociative database) from the top down through the branches of that TCGhierarchy until it locates increasingly closer matches 224.

FIG. 18 shows a flowchart of a process for providing full or partialcomparison of linearized TCG in accordance with an embodiment. As shownin FIG. 18, in step 226, a first TCG is either created, based on aninput text, or retrieved from a previously stored TCG in an associativedatabase. In step 227, a second TCG is again either created based on aninput text, or retrieved from a previously stored TCG in an associativedatabase. In step 228, the TCG names (if alphabetical sorting orordering is used), or other TCG identifiers (if a different sorting orordering criteria is used), are compared to quickly determine an exactmatch. If the names (or other sorting criteria) match, then they are thesame TCG, and the process can end prematurely. In step 229, if it isfound that the TCG names are different, then the TCG relationships andvariables in each TCG are compared with one another to determinematches. Since the relationships within each TCG are linearized andsorted, e.g. alphabetically, matches between two TCGs can be quicklydetermined. Partial matches indicate close semantic relationshipsbetween the two TCGs, which can be useful for many purposes, such assuggesting results to search queries, or advertising similar and/orrelated products. In step 230, the results of the TCG match are outputand/or used for some subsequent purpose. It is important to note thatsince the TCG are ordered or sorted either alphabetically by name oraccording to some other sorting or ordering criteria, the comparison ofone TCG against the others does not require backtracking, and theoverall comparison and/or search step is substantially reduced from avalue of approximately N₂ to approximately N iterations (where N is thetotal number of tuples in a TCG against which the comparison is made).

Associative Database for Use in Rendering TCG without Duplication

In accordance with an embodiment, the system can include a component orprocess for storing and retrieving text within a database, the texthaving a semantic rendering or meaning in a TCG, including providing adatabase content and a tuple conceptual graph (TCG) hierarchy, relationhierarchy, and node hierarchy; receiving a TCG rendering of an inputtext expressed as a plurality of sentences or as a query, each of whichincludes a plurality words therein; accessing the database of previouslystored plurality of tuple conceptual graph (TCG) corresponding to othertexts; and matching the tuple conceptual graph (TCG) and the tupleswithin it corresponding to the input text with the database contentaccording to the TCG hierarchy, relation hierarchy, and node hierarchy,to determine an appropriate location for subsets of information withinthe input text within the database.

FIG. 19 shows a flowchart of a process for storing and retrieving textwithin a database, the text having a semantic rendering or meaning in aTCG, in accordance with an embodiment. As described above, in accordancewith an embodiment, an associative database (ADB) is considered adatabase that is capable of housing any types of objects or other datacontents, and is organized in such a manner (generally hierarchically)that one, e.g TCG is below another TCG within a branch of the hierarchyif that (lower or child) TCG is a more specific version of its (higheror parent) TCG. For example, the TCG concept that “animals are brave”may be higher that the TCG concept “cats are brave”, which is a related,but more specific concept, and as such would appear lower within thesame branch of the TCG hierarchy. As the database hierarchy is descendeddown a particular branch, the related TCG become increasingly morespecific. In accordance with an embodiment, this allows a TCG to beadded to the ADB at the top or root of the ADB's hierarchy, andsuccessively compared, using the above-described comparison techniques,to TCG from the top down through the branches of that TCG hierarchyuntil it meets a match, or can be located within the hierarchy as a newTCG in a new location within the ADB. For example, depending on theparticular implementation, a match at a particular level may be the“result” searched for, or it may represent the “new data” to be added tothe database.

As shown in FIG. 19, in step 232, input text, or example in the form ofa user query, is received into the system, and its words are parsed forlinkages using a link grammar lexicon, in accordance with the processdescribed above in FIG. 11. In step 234 the input text is thentransformed into a TCG, in accordance with the process described abovein FIG. 12. In step 236, the TCG corresponding to the input text iscompared with TCGs previously stored in an associative database, usingfull or partial matching techniques as described above in FIG. 13. Thisstep is then repeated down through the hierarchy of the associativedatabase. In accordance with an embodiment the associative databaseincludes a tuple conceptual graph (TCG) hierarchy, relation hierarchy,and node hierarchy, each of which can be stored together, or separately,and used to optimally match the TCG. In step 238, if a match is found,then matching TCG and their corresponding (plain language) equivalentscan be returned to, e.g. the user in the form of a response to theirquery, or depending on the particular implementation, suggestedalternatives, advertised products, recommendations for similarinterests, etc. In step 240, if the TCG is determined to be unique ornot previously stored in the associative database, then depending on theparticular embodiment, the unique TCG or portions thereof are optionallyadded to the associative database to increase its overall knowledge. Byemploying the quicker linearized techniques of comparing, the overallgoal is served faster and therefore becomes doable in “internet speed”.

Association and Searching of Information from Multiple Sources

In accordance with an embodiment, the system can include a component orprocess for discovering, storing and retrieving text within a databaseaccording to a semantic hierarchy, including providing a databasecontent and a tuple conceptual graph (TCG) hierarchy, relationhierarchy, and node hierarchy; receiving an input text which includes aplurality of phrases and words therein; using a set of semantic rules totransform the syntax to a semantic rendering or meaning; comparingtuples in the input text with the database content; and performing TCGjoins where appropriate, based either on partial tuple overlap or overany concept node argument to tuple information which comes fromdifferent sources, to combine content within the database into new TCGreflecting new semantic information not fully or directly present in anyindividual textual source or previously stored in the database. Newknowledge is created either by combinational methods or by deductivemeans of the individual TCG relations by means of a JOIN operation orinference rule lookup and execution, respectively.

FIG. 20 shows a flowchart of a process for storing and retrieving textwithin a database according to a semantic hierarchy of more general thanin accordance with an embodiment. As shown in FIG. 20, in step 242, anassociative database is provided, together with database content and atuple conceptual graph (TCG) hierarchy, relation hierarchy, and nodehierarchy. In step 244, in an offline mode, the system gathers orreceives text information from various sources, and augments theassociative database with new TCGs using the comparison and ADB matchingand/or insertion techniques described above. In step 246, also inoffline mode, the system determines new semantic relationships betweenTCGs, which may not have existed in the original text information butmay be useful in responding to future queries. In step 248, in an onlinemode, input text, or example in the form of a user query, is receivedinto the system, and its words are parsed for linkages using a linkgrammar lexicon and methodology, and then transformed into a TCG, usingthe techniques described above. In step 250, results are generated,using full or partial matching, by comparing the input text with the TCGinformation in the associative database both received from the varioussources, linked during offline mode, or linked at the time of respondingto the input text/query, using, e.g CG JOIN operations. A CG JOINoperation has the effect of taking two or more TCG's that have somethingin common, and joining them to create new information, which can then beused as is, or stored in the database for future use.

Use in Natural Language Query Processor

In accordance with an embodiment, the techniques described above can beused in a natural language query processor. FIG. 21 shows an example ofa natural language query processor 300 in accordance with an embodiment.As shown in FIG. 21, the system can allow for English language discoursebetween a user and a machine, such as an online system or a web-basedinterface, including queries and responses in a natural, Englishlanguage, format. As queries are received, parsed, and transformed to CG(or TCG) using the above-described techniques, and are matched withinthe associative database, additional answers or more content can becontinuously added to the database. Alternative languages can be used asdesired.

Use of Semantic Understanding in Searching and Providing of Content

In accordance with an embodiment, the techniques described above can beused as part of a system and method for use of semantic understanding insearching and providing of content.

FIG. 22 shows a system for use of semantic understanding in searchingand providing of content, in accordance with an embodiment. As shown inFIG. 22, in accordance with an embodiment, a semantic content system 400comprises a Syntactic Parser (SP) 402, or statistical word tokenizer,which can include features such as a link grammar for data retrieval andparsing; a Syntax To Semantics (STS) semantic rule set 404, which caninclude features and rules for algebra-based transformations; and anAssociative Database (ADB) 406 of linearized tuple conceptual graphs(TCG), which can utilize a conceptual graph formalism. Data 408, such asweb page or other content, can be represented within the ADB, enablingboth fast data retrieval in the form of semantic objects and a broadranging taxonomy of content 414, e.g. advertising categories 416, orother content categories 418. Each semantic object contains all therelated terms and phrases articulating a specific subject 420, enablingautomatic categorization of any set of content, such as a given Webpage.

In accordance with an embodiment, the system can be used to semanticallyinterpret an original data 440, such as an original Web page content oran advertisement, and to populate the associate database. When a requestfor new data is received 444, the system uses the information providedwithin the semantic content system to prepare a response to the request446, which can include semantically related content, such as related webpages, content, or advertising. The response content, which issemantically related to the original request (and which can includingcontent such as advertising or other categories of content) can beprovided as a response to the request 452.

This semantic approach can be used in a variety of ways, for example toimprove the ability to serve ads based on the meaning of a website'spage content. By semantically analyzing the web pages, the system canproperly understand and classify the meaning and sentiment of any givendigital text, and accordingly ensure that the web page receives the mostappropriate advertising. The system can also ensure that campaigns areplaced on pages which are contextually relevant to them, whatever theformat and medium. For example, the semantic approach can be used toanalyze an advertiser's ad and the website it links to, in order toidentify the most relevant matches.

FIG. 23 shows a flowchart of a method for use of semantic understandingin searching and providing of content, in accordance with an embodiment.As shown in FIG. 18, in step 502, the system semantically interpret anoriginal data, such as an original Web page, advertisement or othercontent, and uses techniques such as a link grammar, rules, andalgebra-based transformations to populate the associate database. Instep 504, when a request for new data is received, the system uses theinformation provided within the semantic content system to prepare aresponse to the request, which can include semantically related content,such as advertising. In step 506, the response content, which issemantically related to the original request (and which can includecontent such as advertising or other categories of content) can then beprovided as a response to the request.

The present invention may be conveniently implemented using one or moreconventional general purpose or specialized digital computers ormicroprocessors programmed according to the teachings of the presentdisclosure. Appropriate software coding can readily be prepared byskilled programmers based on the teachings of the present disclosure, aswill be apparent to those skilled in the software art.

In some embodiments, the present invention includes a computer programproduct which is a storage medium (media) having instructions storedthereon/in which can be used to program a computer to perform any of theprocesses of the present invention. The storage medium can include, butis not limited to, any type of disk including floppy disks, opticaldiscs, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs,EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or opticalcards, nanosystems (including molecular memory ICs), or any type ofmedia or device suitable for storing instructions and/or data.

The foregoing description of the present invention has been provided forthe purposes of illustration and description. It is not intended to beexhaustive or to limit the invention to the precise forms disclosed. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical application, therebyenabling others skilled in the art to understand the invention forvarious embodiments and with various modifications that are suited tothe particular use contemplated. It is intended that the scope of theinvention be defined by the following claims and their equivalence.

What is claimed is:
 1. A method for a computer to answer a text-basedquestion using one or more sources of information comprising: receivingthe text-based question at the computer from a user input; parsing intotokens the text-based question; semantically interpreting the parsedinto tokenized text, creating a plurality of tuples corresponding to thetext-based question; and comparing the plurality of tuples for thetext-based question to tuples for semantically interpreted text within adatabase content according to a tuple conceptual graph (TCG) hierarchy,relation hierarchy, and node hierarchy, to find full or partial matchesbetween the tuples corresponding to the text-based question and thedatabase content; whereupon if only finding partial matches between thetuples for the text-based question and the tuples for text within thedatabase: performing TCG joins between a plurality of partially matchedtuples from the database based either on partial tuple overlap or overany concept node argument to tuple information which comes fromdifferent texts, to combine content from the database into a new TCGreflecting new semantic information which is not fully or directlypresent in any individual textual source or previously stored in thedatabase; and presenting a text representation of the new TCGs as theanswer to the question.
 2. The method of claim 1, wherein the user inputis from a web page and the answer to the question is presented on a webpage.
 3. The method of claim 1, wherein the semantically interpretedtext within a database is at least in part based on advertising media.4. A system for use of semantic understanding of a real language requestfor information, comprising: a syntactic parser, executable on aprocessor, configured to tokenize words for data retrieval and parsing;a syntax to semantics transformational algebra-based semantic rule setstored in a memory; an associative database of linearized tupleconceptual graphs (TCG) stored in a memory, which represents textualinformation; and a user interface configured to facilitate inputrequests for information from a user to the system, and the system topose questions to a user and allow the user to answer questionspresented by said system; wherein the system, with a processor, (i)semantically interprets the real language request for information usingat least one of the following: link grammar, rules, and algebra-basedtransformation to transform the request to a semantic rendering ormeaning, including creating a plurality of linearized tuplescorresponding to the real language request, and (ii) compares, with aprocessor, the linearized tuples corresponding to the real languagerequest with the associated database of linearized tuple graphs' contentaccording to a TCG hierarchy, relation hierarchy, and node hierarchy, toidentify a match with the processor according to the following rulesstored in a memory: if a match is found, present to the user,information corresponding to the match from the associated database; ifa partial match is found, using the interface, formulate and present aquestion for the user based on the partial match and request morespecific information; and receiving more specific information from theuser, repeat the comparison of the tuples and formulate additionquestions, if necessary, until a complete match is found; andsubsequently present the information corresponding to the match.
 5. Thesystem of claim 4, wherein the user interface comprises web page entriesand display of text on a web page.
 6. A computer-based method foridentifying text from source texts by comparing one or more query tupleconceptual graphs (TCG) for the query text with one or more source textTCGs for one or more source texts, wherein said TCGs each comprise a TCGidentifier and a set of linearized tuples, and the source text TCGs areordered and stored in one or more databases with the sets of source textlinearized tuples sorted according to a sort criteria, comprising:receiving in a memory the one or more query TCGs and the one or moresource text TCGs; ordering the one or more received query TCGs, with afirst processor in communication with the memory, the orderingcomprising sorting the query TCG sets of linearized tuples according tosaid sort criteria; comparing, with a second processor in communicationwith the memory, at least one said ordered query TCG and at least onesaid source text TCG to determine a match, and if a match is found thenidentifying the matched source text TCGs as full or partial match of thequery TCG; and reporting to a receiving system in communication with theprocessor one or more of the full or partial match source text TCGs. 7.The computer-based method of claim 6, wherein the first processor andsecond processor comprise the same processor.
 8. The computer-basedmethod of claim 6, wherein the query text and the source texts are oneor more of web pages, textual articles in digital form or web-basedadvertising media.
 9. The computer-based method of claim 6, wherein saidordering the one or more received query TCGs comprises folding tuplerelationships with the processor into a minimal canonical representationby successively examining and merging sorted tuple relationships andresolving arguments upon ties.
 10. The computer-based method of claim 6,wherein the one or more source text TCGs are received from a databasehaving a sematic rendering for the source text.
 11. The computer-basedmethod of claim 6, further comprising: generating with a third processorthe one or more query TCGs based on the query texts; and transmittingthe one or more query TCGs to the receiving memory.
 12. Thecomputer-based method of claim 11, wherein said generating and saidordering and comparing are performed by a common processor.
 13. Thecomputer-based method of claim 11, wherein said generating and saidordering and comparing are performed by separate processorscommunicating through one or more networks.
 14. The computer-basedmethod of claim 13, wherein said transmitting the one or more query TCGscomprises transmitting through one or more networks.
 15. Thecomputer-based method of claim 11, wherein said reporting comprisestransmitting the matched source text TCGs to the third processor. 16.The computer-based method of claim 6, further comprising: receiving theone or more of the full or partial match candidate TCGs; and if one fullor partial match TCG is received, identifying the matched TCG as a fullor partial match TCG for the query TCG; or if more than one full orpartial match TCG is received, performing TCG joins in accordance withpreset rules to combine the received full or partial match TCGs into newTCGs reflecting new semantic information not fully or directly presentin any individual textual source represented in the received full orpartial match TCGs, each said new TCGs comprising a new TCG identifierand a new set of linearized tuples; and reporting the one or moreidentified full or partial match TCGs to a receiving system.
 17. Thecomputer-based method of claim 16, further comprising identifying textof said one identified full or partial match TCGs as text related to thequery text.
 18. The computer-based method of claim 17, wherein the stepof identifying text is performed by a processor in the receiving systemand the receiving system presents the identified text to a user.
 19. Thecomputer-based method of claim 16, wherein said performing TCG joins inaccordance with preset rules comprises identifying partial tuple overlapin tuples of the same relationship type.
 20. The computer-based methodof claim 16, wherein said performing TCG joins in accordance with presetrules comprises joining tuples over any concept node argument to tupleinformation from different sources.
 21. The computer-based method ofclaim 6, further comprising receiving the one or more of the full orpartial match candidate TCGs; and if one full or partial match TCG isreceived, identifying the matched TCG as a full or partial match TCG forthe query TCG; or if more than one full or partial match TCG isreceived, creating at least one new TCG by inference using inferencerule lookup and execution, said at least one new TCGs reflecting newsemantic information not fully or directly present in any individualtextual source represented in the received full or partial match TCGs,each said new TCGs comprising a new TCG identifier and a new set oflinearized tuples; and reporting the one or more identified full orpartial match TCGs to a receiving system.
 22. A computer-based methodfor identifying text related to a query text by comparing one or morequery tuple conceptual graphs (TCGs) for the query text with one or morecandidate TCGs for a plurality of candidate texts, comprising: receivingthe query text; transforming the query text into a semantic renderingcomprising one or more query TCGs using a set of semantic rules;comparing the one or more query TCGs with plural candidate text TCGsstored in one or more databases; identifying plural candidate text TCGsas matching TCGs based on said comparing; performing TCG joins inaccordance with preset rules to combine the matching TCGs into new TCGsreflecting new semantic information not fully or directly present in anyindividual textual source represented in the matching TCGs; andidentifying text of said matching candidate new TCGs as text related tothe query text.
 23. The computer-based method of claim 22, wherein saidtransforming comprises creating a query TCG identifier and a query setof linearized tuples for said query text.
 24. The computer-based methodof claim 23, further comprising, before said comparing, ordering thequery TCG by sorting the query set of linearized tuples according to asort criteria, said sort criteria being the same sort criteria used toorder stored candidate text TCGs.
 25. The computer-based method of claim24, wherein said sorting further comprises folding tuple relationshipsinto a minimal canonical representation by successively examining andmerging sorted tuple relationships and resolving arguments upon ties.26. The computer-based method of claim 22, wherein said performing TCGjoins in accordance with preset rules comprises identifying partialtuple overlap in tuples of the same relationship type.
 27. Thecomputer-based method of claim 22, wherein said performing TCG joins inaccordance with preset rules comprises joining tuples over any conceptnode argument to tuple information from different sources.